scholarly journals Development and validation of ferroptosis-related lncRNAs signature for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11627
Author(s):  
Jiaying Liang ◽  
Yaofeng Zhi ◽  
Wenhui Deng ◽  
Weige Zhou ◽  
Xuejun Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Risk Group ◽  
Expression Profiles ◽  
Immune Checkpoint Blockade ◽  
High Risk Group ◽  
The Cancer Genome Atlas

Background Hepatocellular carcinoma (HCC) with high heterogeneity is one of the most frequent malignant tumors throughout the world. However, there is no research to establish a ferroptosis-related lncRNAs (FRlncRNAs) signature for the patients with HCC. Therefore, this study was designed to establish a novel FRlncRNAs signature to predict the survival of patients with HCC. Method The expression profiles of lncRNAs were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. FRlncRNAs co-expressed with ferroptosis-related genes were utilized to establish a signature. Cox regression was used to construct a novel three FRlncRNAs signature in the TCGA cohort, which was verified in the GEO validation cohort. Results Three differently expressed FRlncRNAs significantly associated with prognosis of HCC were identified, which composed a novel FRlncRNAs signature. According to the FRlncRNAs signature, the patients with HCC could be divided into low- and high-risk groups. Patients with HCC in the high-risk group displayed shorter overall survival (OS) contrasted with those in the low-risk group (P < 0.001 in TCGA cohort and P = 0.045 in GEO cohort). This signature could serve as a significantly independent predictor in Cox regression (multivariate HR > 1, P < 0.001), which was verified to a certain extent in the GEO cohort (univariate HR > 1, P < 0.05). Meanwhile, it was also a useful tool in predicting survival among each stratum of gender, age, grade, stage, and etiology,etc. This signature was connected with immune cell infiltration (i.e., Macrophage, Myeloid dendritic cell, and Neutrophil cell, etc.) and immune checkpoint blockade targets (PD-1, CTLA-4, and TIM-3). Conclusion The three FRlncRNAs might be potential therapeutic targets for patients, and their signature could be utilized for prognostic prediction in HCC.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

scholarly journals Ferroptosis regulator genes are a favorable biomarker for hepatocellular carcinoma

2021 ◽  
Author(s):  
Yongfei He ◽  
Shuqi Zhao ◽  
Zhongliu Wei ◽  
Xin Zhou ◽  
Tianyi Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Information ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Regulator Genes ◽  
The Relationship

Abstract BackgroundIn this study, we comprehensively analyzed the relationship between ferroptosis regulator genes (FRGs) and prognosis of hepatocellular carcinoma (HCC), determined the prognostics value of FRGs, established a prediction model, and explored the relationship with immunotherapy for HCC.MethodsThe mRNA transcriptional levels and clinical information of HCC were obtained from The Cancer Genome Atlas (TCGA) database. The 24 FRGs were combined with the differential expression genes (DEGs) of HCC for further analysis. The prognostics values of differential FRGs via the construction of model and validation by the Cox regression analysis.ResultThere were three genes (CARS1, FANCD2, and SLC7A11) were identified as independent risk factors for HCC, and a predictive model was constructed based on CARS1, FANCD2, and SLC7A11. The model showed that the low-risk group HCC patients with a more prolonged overall survival (OS) than the high-risk group (P=0.001). The high-risk group with higher expression of FRGs than the low-risk group. Finally, the relations between FGEs and immune infiltration showed that CARS1, FANCD2, and SLC7A11 had a positive relationship with macrophage infiltration. From these, three genes might be the potential therapeutic targets.ConclusionOur study indicated that CARS1, FANCD2, and SLC7A11 might have potential value for therapeutic strategies as practical and reliable prognostic tools for HCC.

scholarly journals Differentially expressed liver exosome-related genes as a candidate prognostic biomarker for hepatocellular carcinoma

2020 ◽  
Author(s):  
Bangyou Zuo ◽  
Haitao Zhao ◽  
Jin Bian ◽  
Junyun Long ◽  
Xu Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Cell Communication ◽  
High Risk Group ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed

Abstract Background The function of exosome includes cell-to-cell communication, neovascularization, and metastasis of cancer cell and drug resistance, which plays an important part in the occurrence and progression of hepatocellular carcinoma (HCC). Because the mechanism in this area is less studied, our goal is to identify exosome-related genes in HCC, establish a reliable prognostic model for liver cancer patients, and explore its underlying mechanisms. Methods The exoRbase database and The Cancer Genome Atlas (TCGA) database were used to analyze differentially expressed genes (DEGs). Cox regression and LASSO analysis were applied to determine DEGs closely related to overall survival (OS). Then the exosome-related prognostic model was constructed in TCGA and validated in the database of International Cancer Genome Consortium (ICGC). Nomogram graph was performed to predict the survival. CIBERSORT was used to estimate the score of different type of immune cells. DEGs related to immunotherapy are used to predict the effect of immunotherapy. Results 48 exosome-related DEGs were obtained and five genes (XPO1, IFI30, FBXO16, CALM1, MORC3) among them were selected to construct predictive model. Then we divided the HCC patients into low-risk and high-risk groups by the best cut-off value according to the X-tile software. The high-risk related to exosome were significantly associated with a poor prognosis. Moreover, the features related to exosome could positively regulate immune response. At the same time, the proportion of T cell regulatory factors (Tregs) and macrophages M2 is higher in the high-risk group, and high-risk group exhibited higher expression of immune checkpoint molecular including PD-L1, PD-L2, TIGIT, and IDO1. Conclusions Overall, our research showed that markers related to exosomes were potential biomarkers for the prognosis of HCC, providing an immunological perspective for the development of precision treatment.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

2020 ◽  
Author(s):  
Li Liu ◽  
She Tian ◽  
Zhu Li ◽  
Yongjun Gong ◽  
Hao Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Principal Component ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

scholarly journals Identification of prognostic hypoxia-related genes signature on the tumor microenvironment in esophageal cancer

2021 ◽  
Vol 18 (6) ◽  
pp. 7743-7758
Author(s):  
Linlin Tan ◽  
◽  
Dingzhuo Cheng ◽  
Jianbo Wen ◽  
Kefeng Huang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
High Risk ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Group ◽  
Roc Curves ◽  
Risk Groups ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas

<abstract> <sec><title>Background</title><p>Hypoxia is a crucial factor in the development of esophageal cancer. The relationship between hypoxia and immune status in the esophageal cancer microenvironment is becoming increasingly important in clinical practice. This study aims to clarify and investigate the possible connection between immunotherapy and hypoxia in esophageal cancer.</p> </sec> <sec><title>Methods</title><p>The Cancer Genome Atlas databases are used to find two types of esophageal cancer cases. Cox regressions analyses are used to screen genes for hypoxia-related traits. After that, the genetic signature is validated by survival analysis and the construction of ROC curves. GSEA is used to compare differences in enrichment in the two groups and is followed by the CIBERSORT tool to investigate a potentially relevant correlation between immune cells and gene signatures.</p> </sec> <sec><title>Results</title><p>We found that the esophageal adenocarcinoma hypoxia model contains 3 genes (PGK1, PGM1, SLC2A3), and the esophageal squamous cell carcinoma hypoxia model contains 2 genes (EGFR, ATF3). The findings demonstrated that the survival rate of patients in the high-risk group is lower than in the lower-risk group. Furthermore, we find that three kinds of immune cells (memory activated CD4+ T cells, activated mast cells, and M2 macrophages) have a marked infiltration in the tissues of patients in the high-risk group. Moreover, we find that PD-L1 and CD244 are highly expressed in high-risk groups.</p> </sec> <sec><title>Conclusions</title><p>Our data demonstrate that oxygen deprivation is correlated with prognosis and the incidence of immune cell infiltration in patients with both types of esophageal cancer, which provides an immunological perspective for the development of personalized therapy.</p> </sec> </abstract>

scholarly journals A Novel Signature Constructed by Immune-Related LncRNA Predicts the Immune Landscape of Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Mengyu Sun ◽  
Tongyue Zhang ◽  
Yijun Wang ◽  
Wenjie Huang ◽  
Limin Xia
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Cox Regression Analysis ◽  
Chemotherapy Drugs

Colorectal cancer (CRC) has the characteristics of high morbidity and mortality. LncRNA not only participates in the progression of CRC through genes and transcription levels, but also regulates the tumor microenvironment and leads to the malignant phenotype of tumors. Therefore, we identified immune-related LncRNAs for the construction of clinical prognostic model. We searched The Cancer Genome Atlas (TCGA) database for original data. Then we identified differentially expressed irlncRNA (DEirlncRNA), which was paired and verified subsequently. Next, univariate analysis, Lasso and Cox regression analysis were performed on the DEirlncRNA pair. The ROC curve of the signature was drawn, and the optimal cut-off value was found. Then the cohort was divided into a high-risk and a low-risk group. Finally, we re-evaluated the signature from different perspectives. A total of 16 pairs of DEirlncRNA were included in the construction of the model. After regrouping according to the cut-off value of 1.275, the high-risk group showed adverse survival outcomes, progressive clinicopathological features, specific immune cell infiltration status, and high sensitivity to some chemotherapy drugs. In conclusion, we constructed a signature composed of immune-related LncRNA pair with no requirement of the specific expression level of genes, which shows promising clinical predictive value in CRC patients.

scholarly journals A Novel Signature to Predict Thyroid Cancer Prognosis and Immune Landscape Using Immune-Related LncRNA Pairs

2021 ◽  
Author(s):  
BO SONG ◽  
Lijun Tian ◽  
Fan Zhang ◽  
Zheyu Lin ◽  
Boshen Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Thyroid Cancer ◽  
High Risk ◽  
Small Molecule ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

Abstract Background: Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. TC is still often overtreated due to a lack of reliable diagnostic biomarkers. Therefore, determining accurate prognostic predictions to stratify TC patients is important.Methods: Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, ICI-related molecules and small-molecule inhibitor efficacy. Results: We identified 30 DEirlncRNA pairs through Lasso regression, and 14 pairs served as the novel predictive signature. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature can predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further found that some commonly used small-molecule inhibitors, such as sunitinib, were related to this new signature. Conclusions: We constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.

scholarly journals An autophagy-related long non-coding RNA signature for patients with colorectal cancer

2021 ◽  
Author(s):  
Dongyan Zhao ◽  
Xizhen Sun ◽  
Sidan Long ◽  
Shukun Yao
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

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